Industrial internet of things with dual front sub platform and control methods thereof

ABSTRACT

The present disclosure discloses an industrial internet of things with a dual front sub platform and a control method, the industrial internet of things includes a user platform, a service platform, a management platform, a sensor network platform, and an object platform that interact in turn. The service platform adopts a centralized layout, and the management platform and the sensor network platform adopt a front sub platform layout. The control method is used for the industrial internet of things.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority of Chinese Patent Application No.202210370869.X, filed on Apr. 11, 2022, the entire contents of which arehereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates to intelligent manufacturing technology,in particular to an industrial internet of things with a dual front subplatform and control methods thereof.

BACKGROUND

In the field of product intelligent manufacturing, the intelligentproduct production line involves a plurality of intelligentmanufacturing equipment. A plurality of intelligent manufacturingequipment are set according to a relationship of upstream anddownstream, and the parts, components, or products to be manufacturedare assembled in turn to form products. In the prior art, the upstreamand downstream intelligent manufacturing equipment needs tocomprehensively consider the unit product manufacturing capacity of eachintelligent manufacturing equipment. For example, when the unit timemanufacturing capacity of the upstream equipment is five, the unit timemanufacturing capacity of the downstream equipment is better to begreater than or equal to five, so as to prevent the workpiecemanufactured by the upstream equipment from accumulating to thedownstream equipment and may not be digested in time. If the unit timemanufacturing capacity of the downstream equipment is less than five, itmay cause the upstream equipment to suspend operation (generally, it maynot be shut down, due to shutting down and restarting, some equipmentconsumes high energy and takes a long time, which also needs preheating,pre-regulation, etc., causing that cost is high and working hours arelong). It may not be operated again until the downstream equipmentcompletes the production of accumulated workpieces, resulting in a largeamount of no-load time for the upstream equipment. When the upstreamequipment stops operation, the upstream equipment also makescorresponding adjustments, resulting in the adjustment of productmanufacturing capacity in the whole production line, which affects theprogress and orderly cooperation of the whole process.

In practical application, when the specified production capacity of theproduct may not be completed within the specified time, it is generallynecessary to increase the manufacturing capacity of workpieces in eachlink. Therefore, it requires to consider how to adjust the overallproduction capacity of the equipment in each link without exceeding amaximum production capacity of the equipment in each link. The processesof processing and manufacturing of accessories, parts, components, andother structures required by the product are more and more complex,which involves the overall coordination of different workshops,different processes, and different equipment, the amount of datainvolved is huge and data types is more, the existing internet of thingscan not meet such a complex and cumbersome application environment anddata processing. As a result, in the prior art, the assembly line mayonly be processed and divided into several small sub areas according toworkshops, processes, or different parts for separately controlling,which cannot achieve overall coordination, and the manager cannotregulate and manage the output of all production equipment of theproduct as a whole.

SUMMARY

The technical problem to be solved by the present disclosure is toprovide an industrial internet of things with a dual front sub platform.Through reasonable operation design, data transmission and dataprocessing design, the internet of things only uses a modified value ofproduct manufacturing capacity/a modifiable value of productmanufacturing capacity as main transmission data, which not onlysimplifies complexity of the data transmission of each production lineequipment, but also reduces the data processing capacity of eachplatform and reduces the demand level of information processing whilemeeting control of the manufacturing capacity of all production lineequipment of the whole production line, it can also reduce theapplication level and requirement of the internet of things.

The present disclosure is realized through the following technicalscheme. The technical scheme provides the industrial internet of thingswith a dual front sub platform. The industrial internet of thingsincludes a user platform, a service platform, a management platform, asensor network platform, and an object platform that interact in turn.The service platform adopts a centralized layout, the managementplatform and the sensor network platform adopt a front sub platformlayout. The centralized layout refers to that the service platformuniformly receives data, uniformly processes the data, and uniformlysends the data; the front sub platform layout refers to that each of themanagement platform and the sensor network platform is provided with ageneral platform and a plurality of sub platforms, the plurality of subplatforms respectively store and process data of different types ordifferent receiving objects sent by a lower platform, and the generalplatform stores, processes, and transmits the data to a upper platformafter summarizing the data of the plurality of sub platforms. A usersends a modification instruction of product manufacturing parametersaccording to production needs; the user platform receives themodification instruction to modify the product manufacturing parameterof a production line, generates a first instruction and sends the firstinstruction to the service platform, and the product manufacturingparameters include a product manufacturing capacity. The serviceplatform receives and processes the first instruction to generate asecond instruction recognized by the management platform and sends thesecond instruction to the general platform of the management platform.The general platform of the management platform receives the secondinstruction and sends the second instruction to a plurality of subplatforms of the management platform at the same time. The plurality ofsub platforms of the management platform perform data processing on thesecond instruction to generate a third instruction recognized by thesensor network platform, and the third instruction is transmitted to thegeneral platform of the sensor network platform through the subplatforms of the management platform, respectively. The general platformof the sensor network platform receives the third instruction and sendsthe third instruction to a plurality of sub platforms of the sensornetwork platform at the same time. The plurality of sub platforms of thesensor network platform integrate the third instruction with data ofreal-time product manufacturing capacity to form different types ofconfiguration files, and send the configuration files to correspondingobject platform. The sub platforms of the sensor network platform areprovided with independent sub platform databases, and the data ofreal-time product manufacturing capacity is real-time data stored incorresponding sub platform databases corresponding to the objectplatform, which is obtained by a product meter. The object platformreceives the configuration files sent by the sub platforms ofcorresponding sensor network platform, performs manufacturing, or sendsout purchase reminders according to the configuration files.

Based on the above industrial internet of things with a dual front subplatform, the sub platforms of a plurality of the sensor networkplatforms correspond to different production line equipment, and eachproduction line equipment is correspondingly configured with the productmeter. The production line equipment stores and classifies data ofmaximum product manufacturing capacity per unit time of equipment andthe data of real-time product manufacturing capacity obtained in realtime by the product meter to the sub platforms of corresponding sensornetwork platform. The sub platforms of the sensor network platformobtain data of modifiable product manufacturing capacity ofcorresponding production line equipment based on the data of maximumproduct manufacturing capacity and the data of real-time productmanufacturing capacity and transmit the data of modifiable productmanufacturing capacity to the general platform of the sensor networkplatform through the corresponding sub platforms. The data of modifiableproduct manufacturing capacity is a difference between the data ofmaximum product manufacturing capacity and the data of real-time productmanufacturing capacity. The general platform of the sensor networkplatform compiles and packages all the data of modifiable productmanufacturing capacity and sends it to the corresponding sub platformsof the management platform. The general platform of the managementplatform receives and analyzes the data of modifiable productmanufacturing capacity of each sub platform of the management platform,compares the data of modifiable product manufacturing capacity of allproduction line equipment, obtains a minimum value of the data ofmodifiable product manufacturing capacity as a final value of modifiableproduct manufacturing capacity, and compiles and transmits the finalvalue of modifiable product manufacturing capacity to the serviceplatform. The service platform receives and analyzes the final value ofmodifiable product manufacturing capacity, decomposes the value ofmodifiable product manufacturing capacity obtained from the analysisaccording to operation rules to form different sub data sets or arrays,maps the sub data sets or the arrays to a data table of modifiableproduct manufacturing capacity to form a data set of modifiable productmanufacturing capacity, and compiles and sends the data set to the userplatform. The data table of modifiable product manufacturing capacity isa data table formulated in the service platform according to theoperation rules for filling in sub data sets or the array.

Based on the above industrial internet of things with a dual front subplatform, the operation rules include: taking natural number less thanor equal to the value of modifiable product manufacturing capacity asmodifiable values, and forming a sequentially sorted array of allmodifiable values; or presetting an allowable modifiable unit capacityby the service platform, multiplying the allowable modifiable unitcapacity with natural number starting from zero, and taking all thevalues whose calculation results are less than the value of modifiableproduct manufacturing capacity as a sub data set. The allowablemodifiable unit capacity is a minimum value of modified productmanufacturing capacity that is allowed for each production lineequipment.

Based on the above industrial internet of things with a dual front subplatform, the sub platforms of the sensor network platform take the dataof real-time product manufacturing capacity as basic data beforemanufacturing is not performed by the production line equipmentaccording to the configuration files. After the object platform performsmanufacturing according to the configuration files and the user platformsends a data rollback instruction, the service platform performs thedata processing on the data rollback instruction and sends it to thegeneral platform of the management platform, the general platform of themanagement platform sends the data rollback instruction to a pluralityof sub platforms of the management platform at the same time, theplurality of sub platforms of the management platform perform the dataprocessing on the data rollback instruction to generate recognizabledata recognized by the sensor network platform, and send therecognizable data to the general platform of the sensor networkplatform. The general platform of the sensor network platform receivesthe data rollback instruction and respectively sends the processed datarollback instruction to each sub platform of the sensor network platformafter performing the data processing on the data rollback instruction.The sub platforms of the sensor network platform receive the datarollback instruction, perform rollback operation with the basic data ineach sub platform as rollback data, send the basic data to theproduction line equipment, and update parameter values of existingproduct manufacturing capacity.

Based on the above industrial internet of things with a dual front subplatform, the plurality of sub platforms of the sensor network platformintegrate the third instruction with data of real-time productmanufacturing capacity to form different types of configuration filesand send the configuration files to the corresponding object platformincluding following operations. The plurality of sub platforms of thesensor network platform extract the modification instruction data of theproduct manufacturing capacity from the third instruction, and obtainparameter values of real-time product manufacturing capacity throughadding modification value of the product manufacturing capacity in themodification instruction data to the data of real-time productmanufacturing capacity by the plurality of sub platforms of the sensornetwork platform, form different types of the configuration files usingthe operation rules of different production line equipment for theparameter values of real-time product manufacturing capacity, and sendsthe configuration files to the corresponding object platform.

Based on the above industrial internet of things with a dual front subplatform, the object platform receives the configuration files sent bythe sub platforms of corresponding sensor network platform and performsmanufacturing according to the configuration files including followingoperations. The production line equipment of the object platform receivethe configuration files as update files sent by the sub platforms ofcorresponding sensor network platform, and update and iterate theparameter value of existing product manufacturing capacity of productionline equipment using the parameter value of real-time productmanufacturing capacity in the configuration file, and the productionline equipment controls the product manufacturing capacity in unit time.

Based on the above industrial internet of things with a dual front subplatform, when the first instruction corresponds to different executiontime, the sub platforms of the management platform write the executiontime into the corresponding third instruction. When the sub platformdatabases of the sensor network platform receive and store the thirdinstruction, the sub platforms of the sensor network platform extractthe execution time using processors of the sub platforms. When the thirdinstruction is integrated with the data of real-time productmanufacturing capacity to form the configuration files, the executiontime is written into the configuration files. After the object platformreceives the configuration files sent by the sub platforms of thecorresponding sensor network platform, the object platform extracts theexecution time and performs manufacturing according to the configurationfiles at the execution time. The processors of sub platforms arerespectively arranged in corresponding gateways of the sub platforms ofthe sensor network platform.

Based on the above dual front sub platform industrial internet ofthings, the plurality of sub platforms of the management platformperform data processing on the second instruction to generate a thirdinstruction recognized by the sensor network platform includingfollowing operation. The plurality of sub platforms of the managementplatform are configured as a stamping management platform, a weldingmanagement platform, a coating management platform, and a generalassembly management platform based on stamping process, welding process,coating process, and general assembly process in an automobileproduction process. The plurality of sub platforms of the managementplatform respectively predicting material loss of a single vehicle, aproduction capacity per unit time, and a safety stock of each materialin each automobile production process. The plurality of sub platforms ofthe management platform determine a production plan and a purchase planby taking the material loss of a single vehicle, the production capacityper unit time, and the safety stock of each material in each automobileproduction process as basic data of a material requirement planningsystem. The plurality of sub platforms of the management platformgenerate the third instruction recognized by the sensor network platformbased on the production plan and the purchase plan.

Based on the above dual front sub platform industrial internet ofthings, the coating management platform predicts the material loss ofthe single vehicle in the coating process including followingoperations. The coating management platform determines, based on productspecification, coating loss per unit area of the coating process, andcoating equipment, coating loss of the single vehicle through a coatingloss prediction model.

Based on the above dual front sub platform industrial internet ofthings, the coating loss prediction model is a multi-classificationmodel.

Based on the above dual front sub platform industrial internet ofthings, an input of the coating loss prediction model further includes adegree of manual proficiency.

Based on the above dual front sub platform industrial internet ofthings, the degree of manual proficiency is determined through aproficiency prediction model based on a count of spraying vehicles perunit time and a qualification rate of spraying vehicles.

Based on the above dual front sub platform industrial internet ofthings, the plurality of sub platforms of the management platformrespectively predict the production capacity per unit time of eachautomobile production process including following operations. Theplurality of sub platforms of the management platform determine, basedon a labor situation of each automobile production process and a presetproduction capacity per unit time of each automobile production process,an adjustment value of the production capacity per unit time of eachautomobile production process through a capacity prediction model. Theplurality of sub platforms of the management platform determine theproduction capacity per unit time of each automobile production processbased on the preset production capacity per unit time of each automobileproduction process and the adjustment value of the production capacityper unit time of each automobile production process.

Based on the above dual front sub platform industrial internet ofthings, the labor situation includes a count of labor and a degree ofmanual proficiency.

Based on the above dual front sub platform industrial internet ofthings, the plurality of sub platforms of the management platform thedetermine the production capacity per unit time of each automobileproduction process based on the preset production capacity per unit timeof each automobile production process and the adjustment value of theproduction capacity per unit time of each automobile production processfurther including following operations. It is determined whether theadjustment value of the production capacity per unit time of eachautomobile production process determined by the capacity predictionmodel is greater than the preset production capacity per unit time ofeach automobile production process. In response to a determination thatthe adjustment value of the production capacity per unit time of eachautomobile production process is greater than the preset productioncapacity per unit time of each automobile production process, the presetproduction capacity per unit time of each automobile production processis taken as a final production capacity per unit time of each automobileproduction process. In response to a determination that the adjustmentvalue of the production capacity per unit time of each automobileproduction process is less than or equal to the preset productioncapacity per unit time of each automobile production process, theadjustment value of the production capacity per unit time of eachautomobile production process is taken as the final production capacityper unit time of each automobile production process.

Based on the above dual front sub platform industrial internet ofthings, the plurality of sub platforms of the management platformperform data processing on the second instruction to generate the thirdinstruction recognized by the sensor network platform further includingfollowing operations. The plurality of sub platforms of the managementplatform send a material purchase reminder according to a relationshipbetween an actual stock and the safety stock in the future. The sensornetwork platform generates the third instruction recognized based on thematerial purchase reminder.

Based on the above dual front sub platform industrial internet ofthings, the present disclosure also discloses a control method for anindustrial internet of things with a dual front sub platform. Thecontrol method comprises: sending a modification instruction of productmanufacturing parameters by a user according to production needs;receiving the modification instruction to modify the productmanufacturing parameter of a production line, generating a firstinstruction and sending the first instruction to the service platform bythe user platform, wherein the product manufacturing parameters includea product manufacturing capacity; receiving and processing the firstinstruction to generate a second instruction recognized by themanagement platform, and sending the second instruction to the generalplatform of the management platform by the service platform; receivingthe second instruction and sending the second instruction to a pluralityof sub platforms of the management platform at the same time by thegeneral platform of the management platform; perform, by the pluralityof sub platforms of the management platform, data processing on thesecond instruction to generate a third instruction recognized by thesensor network platform, and transmitting the third instruction to thegeneral platform of the sensor network platform through the subplatforms of the management platform, respectively; receiving the thirdinstruction, and sending the third instruction to a plurality of subplatforms of the sensor network platform at the same time by the generalplatform of the sensor network platform; integrating the thirdinstruction with data of real-time product manufacturing capacity toform different types of configuration files, and sending theconfiguration files to corresponding object platform by the plurality ofsub platforms of the sensor network platform; the sub platforms of thesensor network platform being provided with independent sub platformdatabases, and the data of real-time product manufacturing capacitybeing real-time data stored in corresponding sub platform databasescorresponding to the object platform, which is obtained by a productmeter; and receiving the configuration files sent by the sub platformsof corresponding sensor network platform, and performing manufacturing,or sending purchase reminders according to the configuration files bythe object platform.

Based on the above control method for the industrial internet thingswith dual front sub platform, the control method further comprises: thesub platforms of the plurality of sensor network platforms correspondingto different production line equipment, and each production lineequipment being correspondingly configured with the product meter;storing and classifying a data of maximum product manufacturing capacityper unit time of the equipment and the data of real-time productmanufacturing capacity obtained in real time by the product meter to thesub platform of the corresponding sensor network platform by theproduction line equipment; obtaining the data of modifiable productmanufacturing capacity of the corresponding production line equipmentbased on the data of maximum product manufacturing capacity and the dataof real-time product manufacturing capacity, and transmitting the dataof modifiable product manufacturing capacity to the general platform ofthe sensor network platform through the corresponding sub platform bythe sub platform of the sensor network platform; and the data ofmodifiable product manufacturing capacity being a difference between thedata of maximum product manufacturing capacity and the data of real-timeproduct manufacturing capacity; compiling and packaging all the data ofmodifiable product manufacturing capacity and sending it to thecorresponding sub platform in the management platform by the generalplatform of the sensor network platform; receiving and analyzing thedata of modifiable product manufacturing capacity of each sub platform,comparing the data of modifiable product manufacturing capacity of allproduction line equipment, obtaining a minimum value of the data ofmodifiable product manufacturing capacity as a final value of modifiableproduct manufacturing capacity, and compiling and transmitting the finalvalue of modifiable product manufacturing capacity to the serviceplatform by the general platform of the management platform; receivingand analyzing the final value of modifiable product manufacturingcapacity, decomposing the value of modifiable product manufacturingcapacity obtained from the analysis according to the operation rules toform different sub data sets or arrays, mapping the sub data sets orarrays to a data table of modifiable product manufacturing capacity,forming a data set of modifiable product manufacturing capacity, andcompiling and sending it to the user platform by the service platform;and the modifiable data table of product manufacturing capacity being adata table formulated according to the operation rules in the serviceplatform for filling in sub data sets or array data.

Based on the above control method for the industrial internet thingswith dual front sub platform, the plurality of sub platforms of themanagement platform perform data processing on the second instruction togenerate the third instruction recognized by the sensor network platformincluding following operations. The plurality of sub platforms of themanagement platform are configured as a stamping management platform, awelding management platform, a coating management platform, and a finalassembly management platform based on a stamping process, a weldingprocess, a coating process, and a general assembly process in anautomobile production process. The plurality of sub platforms of themanagement platform respectively predict material loss of a singlevehicle, a production capacity per unit time, and a safety stock of eachmaterial in each automobile production process, determine a productionplan and a purchase plan by taking the material loss of a singlevehicle, the production capacity per unit time and the safety stock ofeach material in each automobile production process as a basic data of amaterial requirement planning system, and generate the third instructionrecognized by the sensor network platform based on the production planand the purchase plan.

One or more embodiments of this present disclosure provide anon-transitory computer-readable storage medium for storing computerinstructions, when executed by at least one processor, causing the atleast one processor to perform the above control method.

Compared with the prior art, the beneficial effects of the presentdisclosure are as follows: in order to coordinate the productmanufacturing capacity of all production line equipment to ensure thatall production line equipment can reasonably increase or reduceproduction, the sensor network platform and management platform adoptsthe front sub platform layout, the upper and lower data are uniformlysorted and sent through the general platform, and the different subplatforms are used as channels of the data processing or the datatransmission of different production line equipment, so as to store andprocess a large amount of data by classification, reduce the overalldata processing capacity and calculation pressure of the platform. Thedata is processed by the sensor network platform to form differentconfiguration files, so as to realize the data classification andrecognition of all production line equipment. The data of modifiableproduct manufacturing capacity as data is uploaded to further reduce thedata transmission capacity on the premise of ensuring the reasonableadjustment of all production line equipment, so as to realize safe andfast product manufacturing control and data sending and receiving.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings described herein are used to provide a furtherunderstanding of the embodiments of the present disclosure, which form apart of the present application, and do not constitute a limitation ofthe embodiments of the present disclosure, wherein:

FIG. 1 is a structural frame diagram of an industrial internet of thingswith dual front sub platform according to some embodiments of thepresent disclosure;

FIG. 2 is a flowchart of a control method for the industrial internet ofthings with dual front sub platform according to some embodiments of thepresent disclosure;

FIG. 3 is an exemplary flowchart of a plurality of sub platforms of themanagement platform performing data processing on the second instructionto generate a third instruction recognized by the sensor networkplatform according to some embodiments of the present disclosure;

FIG. 4 is a schematic diagram of a structure of a coating lossprediction model according to some embodiments of the presentdisclosure;

FIG. 5 is a schematic diagram of a structure of a capacity predictionmodel according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In order to make the purpose, technical scheme, and advantages of thepresent disclosure more clear, the present disclosure is furtherdescribed in detail below in combination with the embodiments anddrawings. The schematic embodiments and descriptions of the presentdisclosure are only used to explain the present disclosure and are notused as a limitation of the present disclosure.

As used herein, the singular forms “a,” “an,” and “the” may be intendedto include the plural forms as well, unless the context clearlyindicates otherwise; and the plural forms may be intended to include thesingular forms as well, unless the context clearly indicates otherwise.

As shown in FIG. 1 , some embodiments of the present disclosure aim toprovide an industrial internet of things with dual front sub platform,the industrial internet of things includes a user platform, a serviceplatform, a management platform, a sensor network platform and an objectplatform that interact in turn. The service platform adopts acentralized layout, and the management platform and the sensor networkplatform adopt a front sub platform layout. The centralized layoutrefers to that the service platform uniformly receives data, uniformlyprocesses the data, and uniformly sends the data. The front sub platformlayout refers to that each management platform and the sensor networkplatform is provided with a general platform and a plurality of subplatforms, the plurality of sub platforms respectively store and processdata of different types or different receiving objects sent by a lowerplatform, and the general platform stores, processes, and transmits thedata to a upper platform after summarizing the data of the plurality ofsub platforms. A user sends a modification instruction of productmanufacturing parameters according to production needs. The userplatform receives the modification instruction to modify the productmanufacturing parameter of a production line, generates a firstinstruction and sends the first instruction to the service platform. Theproduct manufacturing parameters include a product manufacturingcapacity. The service platform receives and processes the firstinstruction to generate a second instruction recognized by themanagement platform and sends the second instruction to the generalplatform of the management platform. The general platform of themanagement platform receives the second instruction and sends the secondinstruction to a plurality of sub platforms of the management platformat the same time. The plurality of sub platforms of the managementplatform perform data processing on the second instruction to generate athird instruction recognized by the sensor network platform, and thethird instruction is transmitted to the general platform of the sensornetwork platform through the sub platforms of the management platform,respectively. The general platform of the sensor network platformreceives the third instruction and sends the third instruction to aplurality of sub platforms of the sensor network platform at the sametime. The plurality of sub platforms of the sensor network platformintegrate the third instruction with data of real-time productmanufacturing capacity to form different types of configuration files,and send the configuration files to corresponding object platform. Thesub platforms of the sensor network platform are provided withindependent sub platform databases, and the data of real-time productmanufacturing capacity is real-time data stored in corresponding subplatform databases corresponding to the object platform, which isobtained by a product meter. The object platform receives theconfiguration files sent by the sub platforms of corresponding sensornetwork platform, performs manufacturing, or sends purchase remindersaccording to the configuration files.

It should be noted that as the physical architecture of the industrialinternet of things with dual front sub platform, it is specifically asfollows: the user platform is configured as a terminal device, whichinteracts with the user. The service platform is configured as a firstserver, which receives an instruction from the user platform and sendsit to the management platform, extracts information required forprocessing the user platform from the management platform and sending itto the user platform. The management platform is configured as a secondserver, which controls the operation of the object platform and receivesthe feedback data of the object platform. The sensor network platform isconfigured as a communication network and a gateway for interactionbetween the object platform and the management platform. The objectplatform is configured as a production line equipment to performmanufacturing and/or sends purchase reminders, and a product meter.Since this part belongs to a more common architecture in the prior art,descriptions of the embodiment are not repeated.

In the prior art, in the field of intelligent manufacturing technology,the production process of products and their accessories involves moreproduction line equipment, each production line equipment has a maximumproduct manufacturing capacity per unit time, each production lineequipment may operate according to an actual product manufacturingcapacity, in which the actual product manufacturing capacity is lessthan or equal to the maximum product manufacturing capacity. When it isnecessary to adjust the manufacturing capacity of a production lineequipment or all production line equipment, it is necessary to considerthat each production line equipment may not exceed its maximum productmanufacturing capacity after adjustment. After the adjustment of asingle production line equipment, it is also necessary to consider itsimpact on other production line equipment on the production line. Due tothe large count of production line equipment, the data integration andclassification in the existing technology are not only large inprocessing capacity, but also irregular in classification, which is verydifficult to implement, resulting in the inability to realize theunified regulation of the whole production line equipment in theexisting technology. It often requires a plurality of systems ofinternet of things classified separately, which undoubtedly increasesthe cost and system complexity.

The industrial internet of things with dual front sub platform in theembodiment first uses the independently arranged service platform toprocess all instructions and uploaded data, so as to facilitate dataintegration and data manipulation, and facilitate the coordinated andunified processing of the user platform, so that the service platformand/or user platform can better control the internet of things. Whilethe management platform adopts the front sub platform layout, it may useits general platform and service platform for data interaction and useits different sub platforms for data transmission and processing, so asto fully share the overall data processing capacity of the managementplatform and ensure that the data transmission of different subplatforms may correspond to different object platforms. Similarly, thesensor network platform adopts the front sub platform layout, thegeneral platform of the sensor network platform may be used to integrateand upload data or distribute and decompose the data to thecorresponding sub platform according to different objects, and the subplatform may be used to process the collected data or received data, soas to unify the data format of different object platforms and keepconsistent with the data of the management platform, realize theintegration of different data sources of different object platforms,simplify the data interaction format conversion of different objectplatforms, and reduce data processing.

It should be noted that the user platform in the embodiment may be adesktop computer, a tablet computer, a notebook computer, a mobilephone, or other electronic devices that can realize data processing anddata communication, which is not limited here. In specific applications,the first server and the second server may adopt a single server or aserver cluster, which is not too limited here. It should be understoodthat a process of the data processing mentioned in the embodiment may beprocessed by the processor of the server, and the data stored in theserver may be stored on the storage device of the server, such as a harddisk and other memory. In specific applications, the sensor networkplatform may adopt a plurality of groups of gateway servers or aplurality of groups of intelligent routers, which are not limited here.It should be understood that the process of the data processingmentioned in the embodiment of the present disclosure may be processedby the processor of the gateway server, and the data stored in thegateway server may be stored in the storage device of the gatewayserver, such as the hard disk and other memories such as a solid statedrive (SSD).

In some embodiments, the production line equipment is all kinds ofproduction line equipment relied on in the assembly line of productmanufacturing. Taking mechanical products as an example, the productionline equipment may be part assembly equipment, general assemblyequipment, testing equipment, etc. Further, taking the automobile engineassembly line as an example, the production line equipment may becylinder block processing equipment, cylinder block positioning andturnover equipment, cam assembly installation equipment, bolt assemblyinstallation equipment, machine filter assembly, and oiling equipment,etc. Similarly, the product meter is used to measure the completion ofworkpieces within the unit time in the corresponding production lineequipment, which may be various mechanical or electronic counters.

In some embodiments, modifiable data set of the product manufacturingcapacity is modifiable data set of the final product manufacturingcapacity, which is modification data of the manufacturing capacityobtained by comprehensively considering all production line equipmentwithout affecting the normal manufacturing of all production lineequipment. Specifically, the sub platforms of a plurality of the sensornetwork platforms correspond to different production line equipment, andeach production line equipment is correspondingly configured with theproduct meter. The production line equipment stores and classifies dataof maximum product manufacturing capacity per unit time of equipment andthe data of real-time product manufacturing capacity obtained in realtime by the product meter to the sub platforms of corresponding sensornetwork platform. The sub platforms of the sensor network platformobtain data of modifiable product manufacturing capacity ofcorresponding production line equipment based on the data of maximumproduct manufacturing capacity and the data of real-time productmanufacturing capacity, and transmit the data of modifiable productmanufacturing capacity to the general platform of the sensor networkplatform through the corresponding sub platforms. The data of modifiableproduct manufacturing capacity is a difference between the data ofmaximum product manufacturing capacity and the data of real-time productmanufacturing capacity. The general platform of the sensor networkplatform compiles and packages all the data of modifiable productmanufacturing capacity and sends it to the corresponding sub platformsof the management platform. The general platform of the managementplatform receives and analyzes the data of modifiable productmanufacturing capacity of each sub platform of the management platform,compares the data of modifiable product manufacturing capacity of allproduction line equipment, obtains a minimum value of the data ofmodifiable product manufacturing capacity as a final value of modifiableproduct manufacturing capacity, and compiles and transmits the finalvalue of modifiable product manufacturing capacity to the serviceplatform. The service platform receives and analyzes the final value ofmodifiable product manufacturing capacity, decomposes the value ofmodifiable product manufacturing capacity obtained from the analysisaccording to operation rules to form different sub data sets or arrays,maps the sub data sets or the arrays to a data table of modifiableproduct manufacturing capacity to form a data set of modifiable productmanufacturing capacity, and compiles and sends the data set to the userplatform. The data table of modifiable product manufacturing capacity isa data table formulated in the service platform according to theoperation rules for filling in sub data sets or the array.

In the embodiment, by obtaining a maximum data of the productmanufacturing capacity and data of real-time product manufacturingcapacity of the production line equipment in unit time respectively, thedata of modifiable product manufacturing capacity of the correspondingproduction line equipment may be obtained, and then the modifiableproduct manufacturing capacity data of all production line equipment maybe compared to determine the minimum data of modifiable productmanufacturing capacity, different sub data sets or arrays may be formedwithin the range of product manufacturing capacity of this data, so asto select reasonable values for adjustment.

It is further illustrated that when installing cams in the automobileengine assembly line, it specifically includes eight sub processes:loosing tile cover 01, removing tile cover 02, installing upper andlower shaft tiles 03, installing piston cooling nozzle 04, insertingcamshaft drive key 05, installing camshaft thrust plate 06, lifting andplacing crankshaft 07, and driving key 08. It is assumed that aproduction line equipment is set for each process, which is numberedaccording to the order of 01-08, the specific parameters of eachproduction line are shown in Table 1 below:

TABLE 1 Specific parameters of production line equipment Mini- Sub mumdata Array value of sets of of Maxi- modi- modi- modi- modi- mumReal-time fiable fiable fiable fiable product product product productproduct product manu- manu- manu- manu- manu- manu- fac- fac- fac- fac-fac- fac- Num- turing turing turing turing turing turing ber capacitycapacity capacity capacity capacity capacity 01 32 24 8 7 0; 2; 4; 6 0;1; 2; 02 37 26 11 (allow- 3; 4; 5; 03 43 35 8 able 6; 7 04 44 37 7 modi-05 55 42 13 fiable 06 45 37 8 unit 07 36 29 7 capacity 08 55 44 11 is 2)

It can be seen from table 1 that after comparing the minimum valuesamong the eight production line equipment in 01-08, the minimummodifiable product manufacturing capacity in all production lineequipment is 7, so 7 is taken as the final modifiable productmanufacturing capacity. When the manufacturing capacity of allproduction line equipment in 01-08 is increased, it may not exceed themaximum product manufacturing capacity of all production line equipment,so as to ensure safety regulation. The data is only used as processingsource of the subsequent data, which can also reduce the huge dataprocessing capacity brought by a plurality of parameters of differentproduction line equipment.

In some embodiments, the value of modifiable product manufacturingcapacity obtained from the analysis is decomposed according to theoperation rules to form different sub data sets or arrays. The operationrules are as follows: taking natural number less than or equal to thevalue of modifiable product manufacturing capacity as the modifiablevalues, and forming a sequentially sorted array of all modifiablevalues; or presetting an allowable modifiable unit capacity by theservice platform, multiplying the allowable modifiable unit capacitywith the natural number starting from zero, and taking all the valueswhose calculation results are less than the value of modifiable productmanufacturing capacity as a sub data set, and the allowable modifiableunit capacity being a minimum value of modified product manufacturingcapacity that is allowed for each production line equipment.

Taking Table 1 as an example, when the modifiable product manufacturingcapacity is 7, the natural number less than or equal to 7 includes 0, 1,2, 3, 4, 5, 6, and 7. Thus, the above values are taken as modifiablevalues to form an array, and the user platform may select within a rangeof the array. Similarly, when the modifiable product manufacturingcapacity is 7 and the allowable modifiable unit capacity is set to be 2,a data set including 0, 2, 4, and 6 may be formed, and the user platformmay select an increased product manufacturing capacity in the data set.

In some embodiments, after modifying all production line equipment tocomplete the current product manufacturing task, when it is necessary torestore the starting manufacturing capacity, it may be restored by thefollowing methods: the sub platforms of the sensor network platform takethe data of real-time product manufacturing capacity as the basic databefore manufacturing is not performed by the production line equipmentaccording to the configuration files; after the object platform performsmanufacturing according to the configuration file and the user platformsends a data rollback instruction, the service platform performs thedata processing on the data rollback instruction and sends it to thegeneral platform of the management platform. The general platform of themanagement platform sends the data rollback instruction to a pluralityof sub platforms of the management platform at the same time, theplurality of sub platforms of the management platform perform the dataprocessing on the data rollback instruction to generate a recognizabledata recognizable by the sensor network platform and send therecognizable data to the general platform of the sensor networkplatform. The general platform of the sensor network platform receivesthe data rollback instruction and respectively sends the processed datarollback instruction to each sub platform of the sensor network platformafter performing the data processing on the data rollback instruction.The sub platforms of the sensor network platform receive the datarollback instruction, perform rollback operation with the basic data ineach sub platform as rollback data, send the basic data to theproduction line equipment and update and cover the parameter value ofexisting product manufacturing capacity.

In some embodiments, the plurality of sub platforms of the sensornetwork platform integrate the third instruction with the data ofreal-time product manufacturing capacity to form different types ofconfiguration files, and send the configuration files to thecorresponding object platform including: extracting modificationinstruction data of the product manufacturing capacity from the thirdinstruction, and obtaining parameter values of real-time productmanufacturing capacity through adding modification value of the productmanufacturing capacity in the modification instruction data to the dataof real-time product manufacturing capacity by the plurality of subplatforms of the sensor network platform; forming different types of theconfiguration files using the operation rules of different productionline equipment for the parameter values of real-time productmanufacturing capacity, and sending the configuration files to thecorresponding object platform. Through the above operations, a pluralityof sub platforms of the sensor network platform may convert the thirdinstruction into the parameter value of real-time product manufacturingcapacity, so that the production line equipment may directly read anduse the configuration file, further simplifying the difficulty of datainteraction of production line equipment.

In some embodiments, the object platform receives the configuration filesent by the sub platform of the corresponding sensor network platformand performs manufacturing according to the configuration fileincluding: receiving the configuration files as update files sent by thesub platforms of corresponding sensor network platform by the productionline equipment of the object platform, and updating and iterating theparameter value of existing product manufacturing capacity of productionline equipment using the parameter value of real-time productmanufacturing capacity in the configuration file by the production lineequipment of the object platform, and the production line equipmentcontrols the product manufacturing capacity in unit time.

In some embodiments, the count of workpieces manufactured by differentproduction line equipment per unit time is different, in order tominimize the impact of modified manufacturing capacity on all productionline equipment, it is best to modify the manufacturing capacity ofproduction line equipment with small manufacturing capacity first.Therefore, it is necessary for different production line equipment tomodify the manufacturing capacity according to different executiontimes, which may be executed by the following method. When the firstinstruction corresponds to different execution time, the sub platformsof the management platform write the execution time into thecorresponding third instruction. When the sub platform databases of thesensor network platform receive and store the third instruction, the subplatforms of the sensor network platform extract the execution timeusing processors of the sub platforms. When the third instruction isintegrated with the data of real-time product manufacturing capacity toform the configuration files, the execution time is written into theconfiguration files. After the object platform receives theconfiguration files sent by the sub platforms of the correspondingsensor network platform, the object platform extracts the execution timeand performs manufacturing according to the configuration files at theexecution time. The processors of sub platforms are respectivelyarranged in corresponding gateways of the sub platforms of the sensornetwork platform.

As shown in FIG. 2 , some embodiments of the present disclosure aim toprovide a control method for the industrial internet of things with dualfront sub platform, the industrial internet of things includes a userplatform, a service platform, a management platform, a sensor networkplatform and an object platform that interact in turn. The serviceplatform adopts a centralized layout, and the management platform andthe sensor network platform adopt a front sub platform layout. Thecentralized layout refers to that the service platform uniformlyreceives data, uniformly processes the data, and uniformly sends thedata. The front sub platform layout refers to that each managementplatform and the sensor network platform is provided with a generalplatform and a plurality of sub platforms, the plurality of subplatforms respectively store and process data of different types ordifferent receiving objects sent by a lower platform, and the generalplatform stores, processes, and transmits the data to a upper platformafter summarizing the data of the plurality of sub platforms. A usersends a modification instruction of product manufacturing parametersaccording to production needs. The user platform receives themodification instruction to modify the product manufacturing parameterof a production line, generates a first instruction and sends the firstinstruction to the service platform. The product manufacturingparameters include a product manufacturing capacity. The serviceplatform receives and processes the first instruction to generate asecond instruction recognized by the management platform and sends thesecond instruction to the general platform of the management platform.The general platform of the management platform receives the secondinstruction and sends the second instruction to a plurality of subplatforms of the management platform at the same time. The plurality ofsub platforms of the management platform perform data processing on thesecond instruction to generate a third instruction recognized by thesensor network platform, and the third instruction is transmitted to thegeneral platform of the sensor network platform through the subplatforms of the management platform, respectively. The general platformof the sensor network platform receives the third instruction and sendsthe third instruction to a plurality of sub platforms of the sensornetwork platform at the same time. The plurality of sub platforms of thesensor network platform integrate the third instruction with data ofreal-time product manufacturing capacity to form different types ofconfiguration files, and send the configuration files to correspondingobject platform. The sub platforms of the sensor network platform areprovided with independent sub platform databases, and the data ofreal-time product manufacturing capacity is real-time data stored incorresponding sub platform databases corresponding to the objectplatform, which is obtained by a product meter. The object platformreceives the configuration files sent by the sub platforms ofcorresponding sensor network platform, performs manufacturing, or sendsout purchase reminders according to the configuration files.

The following describes the industrial internet of things with dualfront sub platform and its control method by taking the example that theautomobile production process determines the product manufacturingparameters of the production line through a material requirementplanning system.

Automobile production process may include stamping process, weldingprocess, coating process and general assembly process.

Material requirement planning (MRP) refers to that a backward plan ismade according to the length of the lead time the subordination andcapacity relationship of items at all levels according to the productstructure, by taking each item as the planning object and taking thecompletion period as the time benchmark, and the order of release timeof each item is distinguished according to the length of the lead time,which is a material planning management mode in industrial manufacturingenterprises. The material requirement planning system is a managementinformation system based on logistics demand planning. Production planand purchase plan may be determined by inputting the basic data into theMRP system.

Production plan refers to the plan that the enterprise makes overalllayouts for production tasks and specifically formulates the variety,quantity, quality and progress of production products. Purchase planrefers to the predictable layout and deployment of material purchasemanagement activities during the planning period.

In some embodiments, the user may send a modification instruction ofproduct manufacturing parameter according to the production need, theuser platform may receive the modification instruction, modify productmanufacturing parameters of a production line, generate a firstinstruction, and send the first instruction to the service platform.Production needs may be determined according to the main productionplan. The main production plan refers to all kinds of products and spareparts produced within a planned period of time.

The service platform may receive and process the first instruction,generate a second instruction recognized by the management platform andsend it to the general platform of the management platform.

The general platform of the management platform may receive the secondinstruction and send the second instruction to a plurality of subplatforms of the management platform at the same time. A plurality ofsub platforms of the management platform may perform the data processingon the second instruction to generate the third instruction that may berecognized by the sensor network platform, and the third instruction istransmitted to the general platform of the sensor network platformthrough the sub platforms of the management platform.

A plurality of sub platforms of the management platform may beconfigured as a stamping management platform, a welding managementplatform, a coating management platform and a general assemblymanagement platform. The material loss of a single vehicle, a productioncapacity per unit time, and a safety stock of each material in eachautomobile production process may be predicted respectively by theplurality of sub-platforms of the management platform. A plurality ofsub platforms of the management platform may take the productioncapacity per unit time, material loss of a single vehicle, safety stockof each material, master production plan, and actual stock in eachautomobile production process as the basic data of the materialrequirement planning system to determine the production plan andpurchase plan. A plurality of sub platforms of the management platformmay generate the third instruction recognized by the sensor networkplatform based on the production plan and purchase plan.

A plurality of sub platforms of the management platform may sendmaterial purchase reminders according to the relationship between actualstock and safety stock in the future. A plurality of sub platforms ofthe management platform may generate the third instruction recognized bythe sensor network platform based on the material purchase reminders.

The general platform of the sensor network platform may receive thethird instruction and send the third instruction to a plurality of subplatforms of the sensor network platform at the same time.

A plurality of sub platforms of the sensor network platform mayintegrate the third instruction with data of the real-time productmanufacturing capacity to form different types of configuration files,and send the configuration files to the corresponding object platform.The sub platforms of the sensor network platform are provided withindependent sub platform database, and the data of real-time productmanufacturing capacity is the real-time data stored in a correspondingsub platform database corresponding to the object platform, which isobtained by a product meter.

The object platform receives the configuration file sent by the subplatform corresponding to the sensor network platform and performsmanufacturing and/or sends a purchase reminder according to theconfiguration file. The object platform may be configured as stampingmachine tool and robot, automatic welding equipment and robot, heavyindustry spraying robot, general assembly equipment, terminal equipmentand acquisition equipment to provide required data for a plurality ofsub platforms of the management platform.

FIG. 3 is an exemplary flowchart of a plurality of sub platforms of themanagement platform performing data processing on the second instructionto generate a third instruction recognized by the sensor networkplatform according to some embodiments of the present disclosure. Asshown in FIG. 3 , the process 300 includes the following steps. In someembodiments, the process 300 may be executed by a processor.

In step 310, the plurality of sub platforms of the management platformare configured as a stamping management platform, a welding managementplatform, a coating management platform, and a general assemblymanagement platform based on stamping process, welding process, coatingprocess, and general assembly process in an automobile productionprocess.

In some embodiments, taking the automobile production process as anexample, the automobile production process may include stamping process,welding process, coating process and general assembly process.Accordingly, a plurality of sub platforms of the management platform maybe configured as the stamping management platform, the weldingmanagement platform, the coating management platform, and the generalassembly management platform. The object platform may be configured asstamping machine tool and robot, automatic welding equipment and robot,heavy industry spraying robot, final assembly equipment, terminalequipment and acquisition equipment.

In step 320, respectively predicting material loss of a single vehicle,a production capacity per unit time, and a safety stock of each materialin each automobile production process by the plurality of sub platformsof the management platform.

Material loss of a single vehicle refers to the materials lost in eachautomobile production process in the process of producing a singlevehicle. For example, the material loss in the stamping process mayinclude metal material loss. Material loss in the welding process mayinclude welding material loss. The material loss in the coating processmay include coating loss. The material loss in the general assemblyprocess may include the loss of parts and connectors.

Production capacity per unit time refers to the count of piecescompleted in each automobile production process per unit time. Forexample, the production capacity per unit time of the coating processmay be 100 vehicles per day.

The safety stock of materials refers to the material stock that ensuresthe normal and orderly operation of the whole production line. Forexample, the safety stock of paint in the coating process may be 2000 L.

In some embodiments, taking the coating process as an example, when thecoating process is fully automatic operation of heavy industry sprayingrobot without manual participation, the coating management platform maydetermine the coating loss of a single vehicle through the coating lossprediction model based on the product specification, the coating lossper unit area of the coating process, and the situation of the coatingequipment. When the coating process is not fully automatic operation ofheavy industry spraying robot and requires manual participation, theinput of coating loss prediction model may also include a degree ofmanual proficiency.

Product specification refers to the volume and size of the product. Forexample, the product specification may refer to a product of the length,width, and height of the product. In some embodiments, productspecification may be obtained based on a terminal device in the objectplatform.

The coating loss per unit area in the coating process refers to theamount of coating lost per unit area of the vehicle. In someembodiments, the coating loss per unit area in the coating process maybe obtained based on the heavy industry spraying robot in the objectplatform. For example, the average value of the paint lost per unit areaof the vehicle sprayed by the heavy industry spraying robot in historymay be taken as the coating loss per unit area.

The situation of coating equipment refers to the basic situation of theequipment used in the coating process. In some embodiments, thesituation of the coating equipment may include equipment attributeinformation (e.g., equipment model, etc.), equipment working parameters(e.g., shaping air volume, spraying distance, rotating cup speed,spraying flow, etc.), and equipment maintenance and replacementinformation (e.g., equipment service time, whether the equipment hasbeen repaired). In some embodiments, the situation of the coatingequipment may be obtained based on the heavy industry spraying robot inthe object platform.

The coating loss prediction model may be a multi classification model.For more information about the coating loss prediction model, pleaserefer to other parts of the present disclosure, e.g., FIG. 4 and itsrelated description.

In some embodiments, when the coating process is fully automaticoperation of the heavy industry spraying robot without manualparticipation, the coating loss of a single vehicle may be obtainedbased on the collected historical coating loss data of a single vehicle.For example, the average value of the coating loss data of a historicalsingle vehicle may be taken as the coating loss of a single vehicle.

The prediction method of material loss of single vehicle in otherautomobile production processes is similar to that of coating loss ofsingle vehicle in coating process, which will not be repeated here. Theworking parameters of the equipment in the stamping process may includethe closing height of the press, the height of the stretching pad, thepressure, the angle of the air source, the number of sensors, thestroke, etc. The working parameters of the equipment in the weldingprocess may include preloading time, welding time, welding pressure,welding current, preheating current, preheating time, cooling holdingtime, rest time, etc. The working parameters of the equipment in thegeneral assembly process may include the working parameters of thegeneral assembly equipment.

In some embodiments, a plurality of sub platforms of the managementplatform may determine the adjustment value of the production capacityper unit time of each automobile production process through the capacityprediction model based on the labor situation of each automobileproduction process and the preset production capacity per unit time ofeach automobile production process. Then, the production capacity perunit time of each automobile production process is determined based onthe preset production capacity per unit time of each automobileproduction process and the adjustment value of the production capacityper unit time of each automobile production process.

In some embodiments, the processor may input the labor situation of eachautomobile production process and the preset production capacity perunit time of each automobile production process into the capacityprediction model, and then the capacity prediction model outputs theadjustment value of the production capacity per unit time of eachautomobile production process.

The capacity prediction model may be a deep learning model, such as adeep neural networks (DNN), a recurrent neural network (RNN), aconvolutional neural networks (CNN), etc. For more information about thecapacity prediction model, please refer to other parts of the presentdisclosure, e.g., FIG. 5 and its related descriptions.

In some embodiments, a plurality of sub platforms of the managementplatform may determine whether the adjustment value of the productioncapacity per unit time of each automobile production process determinedby the capacity prediction model is greater than the preset productioncapacity per unit time of each automobile production process. Inresponse to a determination that the adjustment value of the productioncapacity per unit time of each automobile production process is greaterthan the preset production capacity per unit time of each automobileproduction process, the preset production capacity per unit time of eachautomobile production process is taken as the general productioncapacity per unit time of each automobile production process. Inresponse to a determination that the adjustment value of the productioncapacity per unit time of each automobile production process is lessthan or equal to the preset production capacity per unit time of eachautomobile production process, the adjustment value of the productioncapacity per unit time of each automobile production process determinedby the capacity prediction model is taken as the final productioncapacity per unit time of each automobile production process. Forexample, the preset production capacity per unit time of a vehiclecoating process is 50 vehicles/hour, if the adjustment value ofproduction capacity per unit time of the coating process is greater than50 vehicles, 50 vehicles/hour may be determined as the productioncapacity per unit time of the coating process; if the adjustment valueof the production capacity per unit time of the coating process is 40vehicles/hour, which is less than the preset production capacity perunit time of the coating process of 50 vehicles/hour, 40 vehicles/hourmay be taken as the production capacity per unit time of the coatingprocess.

In some embodiments, the safety stock of materials may be determinedbased on the material loss of a single vehicle.

In some embodiments, taking the coating process as an example, thesafety stock of the paint may determine the material demand in theproduction time period from the current time to the future time based onthe material loss of a single vehicle, so as to determine the safetystock. For example, the coating loss of a single vehicle is 2 L, theproduction period from the current time to the future time is 1 day, andthe production capacity of the coating process is 1000 vehicles per day.Then the material demand of the production time period from the currenttime to the future time is 2×1×1000=2000 L, which may be directly usedas the safety stock of paint, or may be used as the safety stock ofpaint after appropriate increase (for example, 2100 L).

In some embodiments, the safety stock of paint may be adjusted based onthe confidence determined by the coating loss prediction model. Forexample, if the confidence is high, the accuracy of determined thecoating loss of a single vehicle is high, so the safety stock of paintmay be set lower. If the confidence is low, the accuracy of determinedthe coating loss of a single vehicle is relatively low, so the safetystock of paint may be set higher. For more information about theconfidence determined by the coating loss prediction model, please referto other parts of the present disclosure, e.g., for example, FIG. 4 andits related description.

In step 330, determining a production plan and a purchase plan by takingthe material loss of a single vehicle, the production capacity per unittime, and the safety stock of each material in each automobileproduction process as basic data of a material requirement planningsystem.

Basic data refers to the data of some basic material. In someembodiments, the basic data may include the production capacity per unittime, the material loss of a single vehicle, and the safety stock ofeach material in each automobile production process.

In some embodiments, the basic data also includes a master productionplan and a current stock. Master production plan refers to all kinds ofproducts and spare parts produced within a planned period of time, whichmay be obtained by the terminal device in the object platform. Currentstock refers to the stock at the current time, which may be obtained bythe acquisition equipment in the object platform.

In some embodiments, a plurality of sub platforms of the managementplatform may take the production capacity per unit time, material lossof a single vehicle, safety stock of each material, master productionplan and actual stock in each automobile production process as the basicdata of the material requirement planning system to determine theproduction plan and the purchase plan.

In step 340, generating the third instruction recognized by the sensornetwork platform based on the production plan and the purchase plan.

In some embodiments, a plurality of sub platforms of the managementplatform may generate third instructions recognized by the sensornetwork platform based on production plans and the purchase plans.

In step 350, sending material purchase reminders by a plurality of subplatforms of the management platform according to the relationshipbetween actual stock at a future time and safety stock.

The actual stock at a future time refers to the actual value of thepredicted stock at a certain time after the current time. For example,if the current time is Jan. 1, 2030, the actual stock at the future timemay be the actual value of the predicted stock on Mar. 1, 2030.

In some embodiments, the actual stock at the future time is a resultthat the actual stock at the current time minus the material demand inthe production time period from the current time to the future time, andadds the planned receipt capacity in the time period. For example,taking the paint stock as an example, if the length of the productiontime period from the current time to the future time is 1 day, theactual stock of paint at the current time is set as 3000 L, the demandfor paint in one day is set as 2000 L, and the planned stored capacityin one day is set as 1000 L, so the actual stock of paint in the futuretime is 3000-2000+1000=2000 L.

In some embodiments, the material demand in the production time periodfrom the current time to the future time may be determined based on thelength of the production time period from the current time to the futuretime, the production capacity of the whole vehicle per unit time, andthe material loss of a single vehicle. In some embodiments, the materialdemand in the production time period from the current time to the futuretime is a result that the length of the production time period from thecurrent time to the future time multiples production capacity per unittime of the whole vehicle, and then multiples the material loss of asingle vehicle. For example, taking the paint stock as an example, ifthe length of the production time period from the current time to thefuture time is 1 day, the production capacity of the whole vehicle inone day is set as 1000 and the coating loss of a single vehicle is setas 2 L, the coating demand in the corresponding production time periodfrom the current time to the future time is 1×1000×2=2000 L.

In some embodiments, the production capacity per unit time of the wholevehicle may be the production capacity per unit time of the lastprocess. For example, if the last process is the general assemblyprocess, the production capacity per unit time of the general assemblyprocess is taken as the production capacity per unit time of the wholevehicle.

In some embodiments, a material purchase reminder may be sent when theactual stock in the future time is less than the safety stock. Forexample, taking the coating process as an example, if the safety stockof paint is 120 L and the actual stock in the future time is less than120 L, a material purchase reminder may be sent. In some embodiments, amaterial purchase reminder may be sent based on the daily materialdemand and the actual stock in the future time. For example, taking thecoating process as an example, the actual stock of paint in the futuretime is 400 L, while the paint required for daily production is expectedto be 100 L. At this time, the actual stock in the future time isgreater than the safety stock, but a reminder may be sent as follows:the stock will be consumed within 4 days, please replenish the stock intime.

Step 360: generating a third instruction recognized by the sensornetwork platform based on the material purchase reminder.

In some embodiments, a plurality of sub platforms of the managementplatform may generate a third instruction recognized by the sensornetwork platform based on the material purchase reminder.

The production plan and purchase plan are determined by taking theproduction capacity per unit time, the material loss of a singlevehicle, and the safety stock of each material in each automobileproduction process respectively predicted by a plurality of subplatforms as the basic data of MRP. It may purchase materials ofappropriate quantity and variety, choose the appropriate time to order,maintain the lowest stock level as far as possible, and obtain variousmaterials required for production in time to ensure the timely supply ofproducts required by users.

FIG. 4 is a schematic diagram of a structure of a coating lossprediction model according to some embodiments of the presentdisclosure.

In some embodiments, as shown in FIG. 4 , the coating loss predictionmodel 420 is a multi-classification model, which may include a neuralnetwork model (e.g., CNN, RNN, DNN, etc.).

In some embodiments, as shown in FIG. 4 , when the coating process isfully automatic operation of the heavy industry spraying robot withoutmanual participation, the input of the coating loss prediction model 420may include the product specification 410-1, the coating loss per unitarea of the coating process 410-2, the situation of the coatingequipment 410-3, and the output is the coating loss of a single vehicle430. When the coating process is not fully automatic operation of heavyindustry spraying robot and requires manual participation, the input ofcoating loss prediction model may also include the degree of manualproficiency 410-4.

The degree of manual proficiency refers to the proficiency of manualoperation. In some embodiments, the degree of manual proficiency may bea numerical value or letter that may reflect the manual proficiency. Forexample, the degree of manual proficiency may be expressed by valuesbetween 1-10, letters A-F, or stars. The value is larger, thealphabetical order, or the star is higher, indicating that the degree ofmanual proficiency is higher.

In some embodiments, the degree of manual proficiency may be determinedthrough the proficiency prediction model based on the number of sprayingvehicles per unit time and the qualification rate of spraying vehicles.In some embodiments, the type of proficiency prediction model mayinclude a neural network model (e.g., CNN, RNN, DNN, etc.).

In some embodiments, the proficiency prediction model may be used toprocess the number of spraying vehicles per unit time and thequalification rate of spraying vehicles to determine the degree ofmanual proficiency. For example, the number of spraying vehicles perunit time and the qualification rate of spraying vehicles may be inputinto the proficiency prediction model, and the proficiency predictionmodel outputs the manual proficiency.

In some embodiments, the proficiency prediction model may be trained andacquired based on historical data. The historical data includes thenumber of spraying vehicles per unit time of historical workers and thequalification rate of historical spraying vehicles. The number ofhistorical workers' spraying vehicles per unit time and thequalification rate of historical spraying vehicles may be used astraining samples. The identification of training samples may behistorical degree of manual proficiency. The historical degree of manualproficiency may be determined manually. The training samples withidentification may be input into the initial proficiency predictionmodel, and the parameters of the initial proficiency prediction modelmay be updated through training. When the training model meets thepreset conditions, the training stops and the trained proficiencyprediction model is obtained.

In some embodiments, as shown in FIG. 4 , the parameters of the coatingloss prediction model 420 may be trained by a plurality of groups oflabeled first training samples 440. In some embodiments, a plurality ofgroups of first training samples 440 may be obtained, and each group offirst training samples 440 may include a plurality of training data andlabels corresponding to the training data. The training data may includethe historical product specifications, the coating loss per unit area ofthe historical coating process, and the situation of the historicalcoating equipment. The label of the training data may be the actualvalue of the coating loss of a historical single vehicle.

When the coating loss prediction model 420 is trained, the coating lossof a single vehicle may be divided into several sections (for example, 0L˜20 L, 20 L˜40 L, 40 L˜60 L, 60 L˜80 L, 80 L˜100 L), and then the labelmay be constructed based on the section where the actual value islocated. For example, if the actual value of coating loss of ahistorical single vehicle is 20 L-40 L, the label is [0, 1, 0, 0, 0],that is, the label at the corresponding position of the section is 1 andother positions are 0. Correspondingly, the coating loss of a singlevehicle 430 output by the coating loss prediction model 420 is a vector,and the value in the vector represents the possibility of belonging toeach section. The section with the largest value in the vector is takenas the prediction result of the model, and the output value of thecorresponding section is confidence. For example, the coating lossprediction model 420 may output the probability value of coating loss offive single vehicles, which may be expressed as [0.1, 0.69, 0.05, 0.06,0.1], and the coating loss 20 L˜40 L of a single vehicle correspondingto the highest probability value of 0.69 is the coating loss of a singlevehicle. The highest probability value of 0.69 is the confidence ofcoating loss of a single vehicle.

Through a plurality of groups of first training samples 440, theparameters of the initial coating loss prediction model 450 may beupdated to obtain the trained coating loss prediction model 420. In someembodiments, the parameters of the initial coating loss prediction model450 may be iteratively updated based on a plurality of first trainingsamples so that the loss function of the model meets the presetconditions. For example, the loss function converges, or the lossfunction value is less than the preset value. When the loss functionmeets the preset conditions, the model training is completed, and thetrained initial coating loss prediction model 450 is obtained. Thecoating loss prediction model 420 and the trained initial coating lossprediction model 450 have the same model structure.

In some embodiments, when the input of the coating loss prediction model420 also includes the degree of manual proficiency 410-4, the trainingsample may also include historical degree of manual proficiency.

Through the coating loss prediction model to predict the coating loss ofa single vehicle, the product specification, the coating loss per unitarea of the coating process, and the situation of the coating equipmentmay be taken as the input of the coating loss prediction model, andcombined with the interrelated prediction results of manual proficiency,causing that the coating loss prediction model may predict the coatingloss of a single vehicle more accurately.

FIG. 5 is a schematic diagram of a structure of a capacity predictionmodel according to some embodiments of the present disclosure.

In some embodiments, as shown in FIG. 5 , the input of the capacityprediction model 520 may include the labor situation 510-1 of eachautomobile production process and the preset production capacity perunit time 510-2 of each automobile production process, and the output isthe adjustment value 530 of the production capacity per unit time ofeach automobile production process.

In some embodiments, the labor situation may include the number of laborand the degree of manual proficiency. For more information about thedegree of manual proficiency, please refer to other parts of the presentdisclosure, e.g., FIG. 4 and its related description.

In some embodiments, as shown in FIG. 5 , the parameters of the capacityprediction model 520 may be trained by a plurality of groups of labeledsecond training samples 540. In some embodiments, a plurality of groupsof second training samples 540 may be obtained, and each group of secondtraining samples 540 may include a plurality of training data and labelscorresponding to the training data. The training data may include thehistorical labor situation of each automobile production process and thehistorical preset production capacity per unit time of each automobileproduction process. The historical labor situation of each automobileproduction process and the historical preset production capacity perunit time of each automobile production process are the labor situationand preset production capacity per unit time within the historical timeperiod. The label of training data may be the actual value of historicalproduction capacity per unit time of each automobile production process.

In some embodiments, the parameters of the initial capacity predictionmodel 550 may be iteratively updated based on a plurality of secondtraining samples to make the loss function of the model meet the presetconditions. For example, the loss function converges, or the lossfunction value is less than the preset value. When the loss functionmeets the preset conditions, the model training is completed, and thetrained initial capacity prediction model 550 is obtained. Theproduction capacity prediction model 520 and the trained initialcapacity prediction model 550 have the same model structure.

Through the capacity prediction model to predict the adjustment value ofthe production capacity per unit time of each automobile productionprocess, the labor situation of each automobile production process andthe preset production capacity per unit time of each automobileproduction process may be used as the input of the capacity predictionmodel, so that the capacity prediction model may predict the adjustmentvalue of the production capacity per unit time of each automobileproduction process more accurately. Then, the production capacity perunit time of each automobile production process is more accuratelydetermined based on the relationship between the preset productioncapacity per unit time of each automobile production process and theadjustment value of the production capacity per unit time of eachautomobile production process.

In some embodiments, a computer-readable storage medium may be used tostore computer instructions. When the computer instructions are executedby a processor, the control method for the dual front sub platformindustrial internet things can be realized.

Those skilled in the art may realize that the units and algorithm stepsof each example described in combination with the embodiments disclosedherein can be realized by electronic hardware, computer software or acombination of the two. In order to clearly illustrate theinterchangeability of hardware and software, the composition and stepsof each example have been generally described according to function inthe above description. Whether these functions are performed in hardwareor software depends on the specific application and design constraintsof the technical scheme. Professional technicians may use differentmethods to realize the described functions for each specificapplication, but such realization should not be considered to be beyondthe scope of the present invention.

In several embodiments provided in the present application, it should beunderstood that the disclosed devices and methods may be realized inother ways. For example, the embodiment of device described above isonly schematic. For example, the division of the unit is only a logicalfunction division, and there may be another division mode in actualimplementation. For example, a plurality of units or components may becombined or integrated into another system, or some features may beignored or not executed. In addition, the mutual coupling or directcoupling or communication connection shown or discussed may be indirectcoupling or communication connection through some interfaces, devices orunits, or electrical, mechanical or other forms of connection.

The units described as separate parts may or may not be physicallyseparated. Those skilled in the art can realize that the units andalgorithm steps of the examples described in connection with theembodiments disclosed herein can be implemented in electronic hardware,computer software, or a combination of the two. In order to clearlyillustrate the interchangeability of hardware and software, thecomposition and steps of each example have been generally describedaccording to the function in the above description. Whether thesefunctions are performed in hardware or software depends on the specificapplication and design constraints of the technical scheme. Professionaltechnicians may use different methods to realize the described functionsfor each specific application, but such realization should not beconsidered to be beyond the scope of the present invention.

In addition, each functional unit in each embodiment of the presentdisclosure may be integrated in one processing unit, each unit may existseparately, or two or more units may be integrated in one unit. Theabove integrated units may be realized in the form of hardware orsoftware functional units.

If the integrated unit is realized in the form of software functionalunit and sold or used as an independent product, it may be stored in acomputer-readable storage medium. Based on this understanding, thetechnical solution of the present invention may be embodied in the formof a software product, which is stored in a storage medium, It includesseveral instructions to enable a computer device (which may be apersonal computer, server, grid device, etc.) to perform all or part ofthe steps of the method described in various embodiments of the presentinvention. The aforementioned storage media include: USB flash disk,mobile hard disk, read only memory (ROM), random access memory (RAM),magnetic disc or optical disc and other media that may store programcodes.

The specific embodiments described above further detail the purpose,technical scheme and beneficial effects of the present disclosure. Itshould be understood that the above are only the specific embodiments ofthe present disclosure and are not used to limit the protection scope ofthe present disclosure. Any modification, equivalent replacement,improvement, etc. made within the spirit and principles of the presentdisclosure should be included in the protection scope of the presentdisclosure.

What is claimed is:
 1. A system of an industrial internet of things witha dual front sub platform, comprising a user platform, a serviceplatform, a management platform, a sensor network platform, and anobject platform that interact in turn, wherein the service platformadopts a centralized layout, the management platform and the sensornetwork platform adopt a front sub platform layout; the centralizedlayout refers to that the service platform uniformly receives data,uniformly processes the data, and uniformly sends the data; the frontsub platform layout refers to that each of the management platform andthe sensor network platform is provided with a general platform and aplurality of sub platforms, the plurality of sub platforms respectivelystore and process data of different types or different receiving objectssent by a lower platform, and the general platform stores, processes,and transmits the data to a upper platform after summarizing the data ofthe plurality of sub platforms; the user platform receives from a user,a modification instruction of product manufacturing parameters accordingto production needs, to modify the product manufacturing parameter of aproduction line, generates a first instruction and sends the firstinstruction to the service platform, wherein the product manufacturingparameters include a product manufacturing capacity; the serviceplatform receives and processes the first instruction to generate asecond instruction recognized by the management platform, and sends thesecond instruction to the general platform of the management platform;the general platform of the management platform receives the secondinstruction and sends the second instruction to the plurality of subplatforms of the management platform at the same time; the plurality ofsub platforms of the management platform perform data processing on thesecond instruction to generate a third instruction recognized by thesensor network platform, and the third instruction is transmitted to thegeneral platform of the sensor network platform through the plurality ofsub platforms of the management platform, respectively; the generalplatform of the sensor network platform receives the third instructionand sends the third instruction to the plurality of sub platforms of thesensor network platform at the same time; the plurality of sub platformsof the sensor network platform integrate the third instruction withreal-time product manufacturing data to form different types ofconfiguration files, and send the configuration files to correspondingobject platform, wherein the plurality of sub platforms of the sensornetwork platform are provided with independent sub platform databases,and the real-time product manufacturing data is real-time data stored incorresponding sub platform databases corresponding to the objectplatform, which is obtained by a product meter; the object platformreceives the configuration files sent by the corresponding sub platformsof the sensor network platform, performs manufacturing, or sendspurchase reminders according to the configuration files, wherein thecorresponding sub platforms of the sensor network platform correspond todifferent production line equipment, and each production line equipmentis correspondingly configured with the product meter; the productionline equipment stores and classifies data of maximum productmanufacturing capacity per unit time of equipment and the real-timeproduct manufacturing data obtained in real time by the product meter tothe corresponding sub platforms of the sensor network platform; theplurality of sub platforms of the sensor network platform obtain data ofmodifiable product manufacturing capacity of corresponding productionline equipment based on the data of maximum product manufacturingcapacity and the real-time product manufacturing data, and transmit thedata of modifiable product manufacturing capacity to the generalplatform of the sensor network platform through the corresponding subplatforms, wherein the data of modifiable product manufacturing capacityis a difference between the data of maximum product manufacturingcapacity and the real-time product manufacturing data; the generalplatform of the sensor network platform compiles and packages all thedata of modifiable product manufacturing capacity and sends it to thecorresponding sub platforms of the management platform; the generalplatform of the management platform receives and analyzes the data ofmodifiable product manufacturing capacity of each sub platform of themanagement platform, compares the data of modifiable productmanufacturing capacity of all production line equipment, obtains aminimum value of the data of modifiable product manufacturing capacityas a final value of modifiable product manufacturing capacity, andcompiles and transmits the final value of modifiable productmanufacturing capacity to the service platform; and the service platformreceives and analyzes the final value of modifiable productmanufacturing capacity, decomposes the value of modifiable productmanufacturing capacity obtained from the analysis according to operationrules to form different sub data sets or arrays, maps the sub data setsor the arrays to a data table of modifiable product manufacturingcapacity to form a data set of modifiable product manufacturingcapacity, and compiles and sends the data set to the user platform,wherein the data table of modifiable product manufacturing capacity isformulated in the service platform according to the operation rules forfilling in the sub data sets or the arrays.
 2. The system of theindustrial internet of things of claim 1, wherein the operation rulesinclude: taking natural number less than or equal to the value ofmodifiable product manufacturing capacity as modifiable values, andforming a sequentially sorted array of all modifiable values; orpresetting an allowable modifiable unit capacity by the serviceplatform, multiplying the allowable modifiable unit capacity withnatural number starting from zero, and taking all the values whosecalculation results are less than the value of modifiable productmanufacturing capacity as a sub data set, the allowable modifiable unitcapacity being a minimum value of modified product manufacturingcapacity that is allowed for each production line equipment.
 3. Thesystem of the industrial internet of things of claim 1, wherein theplurality of sub platforms of the sensor network platform take thereal-time product manufacturing data as basic data before manufacturingis not performed by the production line equipment according to theconfiguration files; after the object platform performs manufacturingaccording to the configuration files and the user platform sends a datarollback instruction, the service platform performs the data processingon the data rollback instruction and sends it to the general platform ofthe management platform, the general platform of the management platformsends the data rollback instruction to the plurality of sub platforms ofthe management platform at the same time, the plurality of sub platformsof the management platform perform the data processing on the datarollback instruction to generate recognizable data recognized by thesensor network platform, and send the recognizable data to the generalplatform of the sensor network platform; the general platform of thesensor network platform receives the data rollback instruction andrespectively sends the processed data rollback instruction to each subplatform of the sensor network platform after performing the dataprocessing on the data rollback instruction; and the plurality of subplatforms of the sensor network platform receive the data rollbackinstruction, perform rollback operation with the basic data in each subplatform as rollback data, send the basic data to the production lineequipment, and update parameter values of existing product manufacturingcapacity.
 4. The system of the industrial internet of things of claim 1,wherein the plurality of sub platforms of the sensor network platformintegrate the third instruction with real-time product manufacturingdata to form different types of configuration files and send theconfiguration files to the corresponding object platform, including:extracting modification instruction data of the product manufacturingparameters from the third instruction, and obtaining parameter values ofreal-time product manufacturing capacity through adding modificationvalue of the product manufacturing capacity in the modificationinstruction data to the real-time product manufacturing data by theplurality of sub platforms of the sensor network platform; and formingdifferent types of the configuration files using the operation rules ofdifferent production line equipment for the parameter values ofreal-time product manufacturing capacity, and sending the configurationfiles to the corresponding object platform.
 5. The system of theindustrial internet of things of claim 4, wherein the object platformreceives the configuration files sent by the corresponding sub platformsof the sensor network platform and performs manufacturing according tothe configuration files, including: receiving the configuration files asupdate files, which is sent by the corresponding sub platforms of thesensor network platform by the production line equipment of the objectplatform, and updating and iterating the parameter value of existingproduct manufacturing capacity of production line equipment using theparameter value of real-time product manufacturing capacity in theconfiguration file by the production line equipment of the objectplatform, wherein the production line equipment controls the productmanufacturing capacity in unit time.
 6. The system of the industrialinternet of things of claim 1, wherein when the first instructioncorresponds to different execution time, the plurality of sub platformsof the management platform write the execution time into thecorresponding third instruction; when the sub platform databases of thesensor network platform receive and store the third instruction, theplurality of sub platforms of the sensor network platform extract theexecution time using processors of the plurality of sub platforms; andwhen the third instruction is integrated with the real-time productmanufacturing data to form the configuration files, the execution timeis written into the configuration files, after the object platformreceives the configuration files sent by the corresponding sub platformsof the sensor network platform, the object platform extracts theexecution time and performs manufacturing according to the configurationfiles at the execution time; and the processors of the plurality of subplatforms are respectively arranged in corresponding gateways of theplurality of sub platforms of the sensor network platform.
 7. A controlmethod for an industrial internet of things with a dual front subplatform, the industrial internet of things including a user platform, aservice platform, a management platform, a sensor network platform, andan object platform that interact in turn, wherein the service platformadopts a centralized layout, the management platform and the sensornetwork platform adopt a front sub platform layout; the centralizedlayout refers to that the service platform uniformly receives data,uniformly processes the data, and uniformly sends the data; the frontsub platform layout refers to that each management platform and thesensor network platform is provided with a general platform and aplurality of sub platforms, the plurality of sub platforms respectivelystore and process data of different types or different receiving objectssent by a lower platform, and the general platform stores, processes,and transmits the data to a upper platform after summarizing the data ofthe plurality of sub platforms; the control method comprising: sending,by a user, a modification instruction of product manufacturingparameters according to production needs; receiving, by the userplatform, the modification instruction to modify the productmanufacturing parameter of a production line, generating a firstinstruction and sending the first instruction to the service platform,wherein the product manufacturing parameters include a productmanufacturing capacity; receiving and processing, by the serviceplatform, the first instruction to generate a second instructionrecognized by the management platform, and sending the secondinstruction to the general platform of the management platform;receiving, by the general platform of the management platform, thesecond instruction and sending the second instruction to the pluralityof sub platforms of the management platform at the same time;performing, by the plurality of sub platforms of the managementplatform, data processing on the second instruction to generate a thirdinstruction recognized by the sensor network platform, and transmittingthe third instruction to the general platform of the sensor networkplatform through the plurality of sub platforms of the managementplatform, respectively; receiving, by the general platform of themanagement platform, the third instruction, and sending the thirdinstruction to the plurality of sub platforms of the sensor networkplatform at the same time; integrating, by the plurality of subplatforms of the sensor network platform, the third instruction withreal-time product manufacturing data to form different types ofconfiguration files, and sending the configuration files tocorresponding object platform wherein the plurality of sub platforms ofthe sensor network platform being provided with independent sub platformdatabases, and the real-time product manufacturing data being real-timedata stored in corresponding sub platform databases corresponding to theobject platform, which is obtained by a product meter; receiving, by theobject platform, the configuration files sent by the corresponding subplatforms of the sensor network platform, and performing manufacturing,or sending purchase reminders according to the configuration files,wherein the corresponding sub platforms of the sensor network platformcorresponding to different production line equipment, and eachproduction line equipment being correspondingly configured with theproduct meter; storing and classifying, by the production lineequipment, a data of maximum product manufacturing capacity per unittime of the equipment and the real-time product manufacturing dataobtained in real time by the product meter to the corresponding subplatforms of the sensor network platform; obtaining, by the plurality ofsub platforms of the sensor network platform, the data of modifiableproduct manufacturing capacity of the corresponding production lineequipment based on the data of maximum product manufacturing capacityand the real-time product manufacturing data, and transmitting the dataof modifiable product manufacturing capacity to the general platform ofthe sensor network platform through the corresponding sub platform,wherein the data of modifiable product manufacturing capacity being adifference between the data of maximum product manufacturing capacityand the real-time product manufacturing data; compiling and packaging,by the general platform of the sensor network platform, all the data ofmodifiable product manufacturing capacity and sending it to thecorresponding sub platform in the management platform; receiving andanalyzing, by the general platform of the management platform, the dataof modifiable product manufacturing capacity of each sub platform,comparing the data of modifiable product manufacturing capacity of allproduction line equipment, obtaining a minimum value of the data ofmodifiable product manufacturing capacity as a final value of modifiableproduct manufacturing capacity, and compiling and transmitting the finalvalue of modifiable product manufacturing capacity to the serviceplatform; and receiving and analyzing, by the service platform, thefinal value of modifiable product manufacturing capacity, decomposingthe value of modifiable product manufacturing capacity obtained from theanalysis according to the operation rules to form different sub datasets or arrays, mapping the sub data sets or the arrays to a data tableof modifiable product manufacturing capacity, forming a data set ofmodifiable product manufacturing capacity, and compiling and sending itto the user platform, wherein the modifiable data table of productmanufacturing capacity being formulated according to the operation rulesin the service platform for filling in the sub data sets or the arrays.